Certain embodiments generally relate to methods and systems for electronically evaluating baggage scanned images.
In recent years there has been increasing interest in the use of imaging devices at airports to improve security. Today, thousands of computed tomography (CT) scanners are installed at airports to scan checked baggage. The CT scanners generate data sets that are used to form images representative of each scanned baggage. The data sets are currently processed by an automated image recognition system, such as for certain patterns, characteristics and the like. When the image recognition system identifies a potential threat, the images are brought to the attention of a local operator, for example, who is located at an airport.
The CT scanners, also referred to as explosive detection systems (EDS), are capable of producing full 3-dimensional (3-D) images. However, the software required to view such 3-D images is complex and generally requires sophisticated local operators with expertise in 3-D rendering software tools. CT scanners are able to generate a 3-D voxel data set that represents the volume of the scanned bag. Conventional CT scanners provide 3-D images by stacking a series of closely spaced cross section images into a 3-D matrix. The 3-D images may then be viewed by a local operator/screener. The local operator at the airport terminal usually steps through two-dimensional (2-D) CT slices (e.g., planes) of the 3-D matrix to detect and identify potential threats within the packed bag.
Currently, existing CT based EDS are deployed at airports to detect various threats within packed bags. The suspicious baggages are passed on to a human screener who examines individual 2D CT slice images of the scanned baggage. The CT slice images of alarmed bags are carefully examined by the human screener who then either accepts or redirects the baggage for explosive trace detection (ETD) and/or manual unpacking for a visual inspection.
After the baggage is checked-in, the baggage is scanned by a CT scanner and axial slices or images are created of the baggage. The local operator/screener views the axial slices or images by scrolling through each image slice one by one to determine if any potential threats are present in an image. Scrolling through over dozens of images (or even more for future generation scanners) for each bag is a laborious task, and the local operator/screener must be alert to detect features of any potential threats within an image in order to flag the possible threats. Examination of each axial slice image gives rise to operator/screener fatigue that eventually will lead to sub-optimal performance by the operator causing him/her to miss some threats. The CT 3-D data set of a packed baggage may include hundreds of axial slice images. Of these images only a few images may show the potential threat. If the local operator misses any one of these few images, the undetected threats could result in disaster either while a plane, train, ship, or cargo vessel is in transit or upon arrival at the destination.
There is a need for an improved baggage scanning system and method to allow baggage and cargo to be quickly screened while improving performance in detection of undesired objects, such as contraband, weapons and explosives, both in automated detection systems and systems operated partially or completely by an operator.